Image compression techniques involve exploiting aspects of an image to reduce its overall size while retaining information that can be used to re-establish the image to its original (lossless) or near-original (lossy) form. Different parameters can be provided to compressors to achieve performance characteristics that best-fit particular environments. For example, higher compression ratios can be used to increase the amount of available storage space within computing devices (e.g., smart phones, tablets, wearables, etc.), but this typically comes at a cost of cycle-intensive compression procedures that consume correspondingly higher amounts of power and time. On the contrary, cycle-efficient compression techniques can reduce power consumption and latency, but this typically comes at a cost of correspondingly lower compression ratios and amounts of available storage space within computing devices.
Notably, new compression challenges are arising as computing device capabilities are enhanced over time. For example, computing devices can be configured (e.g., at a time of manufacture) to store thousands of images that are frequently-accessed by users of the computing devices. For example, a collection of emoticons can be stored at a given computing device, where it can be desirable to enable the collection of emoticons to be frequently-accessed with low computational overhead. Additionally, although average storage space availability is also being increased over time, it can still be desirable to reduce a size of the collection to increase the average storage space availability.